In-Distribution Steering: Balancing Control and Coherence in Language Model Generation
Abstract
Activation steering methods control large language model (LLM) behavior by modifying internal activations at inference time. However, most existing activation steering methods rely on a fixed steering strength, leading to either insufficient control or unadapted intervention that degrades text plausibility and coherence. We introduce In-Distribution Steering (IDS), a novel method that adapts steering strength based on the input data distribution in representation space. IDS dynamically adjusts interventions according to how far a given input lies within the distribution, enabling adaptive intervention and generation stability during text generation. Experiments demonstrate that IDS achieves strong accuracy on classification tasks while producing coherent text without collapse, making IDS particularly well suited for real-world applications.
Cite
@article{arxiv.2510.13285,
title = {In-Distribution Steering: Balancing Control and Coherence in Language Model Generation},
author = {Arthur Vogels and Benjamin Wong and Yann Choho and Annabelle Blangero and Milan Bhan},
journal= {arXiv preprint arXiv:2510.13285},
year = {2025}
}